Many classical algorithms [1]}, [2]}, [3]}, [4]}, [5]} heuristically search similar patches for the reconstruction of missing image regions, while it is still very non-trivial for these methods to preserve good textures and holistic structures of the corrupted image.
Benefiting from the excellent capacities of CNNs [6]} and Generative Adversarial Networks (GANs) [7]}, existing deep learning methods [8]}, [9]}, [10]}, [11]}, [12]}
could conduct the image inpainting tasks in some common cases. However, the capability to inpaint realistic image content is often limited in practice by the underlying challenges as follows.
